Initiating test runs based on fault detection results

ABSTRACT

A method and apparatus are provided for initiating test runs based on a fault detection result. The method comprises receiving operational data associated with processing of a workpiece by a processing tool, processing the operational data to determine fault detection results; and causing a test run to be performed based on at least a portion of the fault detection results.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to an industrial process, and, moreparticularly, to initiating test runs in a semiconductor fabricationruns based on fault detection results.

2. Description of the Related Art

There is a constant drive within the semiconductor industry to increasethe quality, reliability and throughput of integrated circuit devices,e.g., microprocessors, memory devices, and the like. This drive isfueled by consumer demands for higher quality computers and electronicdevices that operate more reliably. These demands have resulted incontinual improvements in the manufacture of semiconductor devices,e.g., transistors, as well as in the manufacture of integrated circuitdevices incorporating such transistors. Additionally, reducing thedefects in the manufacture of the components of a typical transistoralso lowers the overall cost per transistor as well as the cost ofintegrated circuit devices incorporating such transistors.

During the fabrication process, various events may take place thataffect the performance of the devices being fabricated. That is,variations in the fabrication process steps may result in deviceperformance variations. Factors, such as feature critical dimensions,doping levels, contact resistance, particle contamination, etc., all maypotentially affect the end performance of the device. Various tools inthe processing line are controlled, in accordance with performancemodels, to reduce processing variation. Commonly controlled toolsinclude photolithography steppers, polishing tools, etching tools, anddeposition tools. Pre-processing and/or post-processing metrology datais supplied to process controllers for the tools. Operating recipeparameters, such as processing time, are calculated by the processcontrollers based on the performance model and the metrology informationto attempt to achieve post-processing results as close to a target valueas possible. Reducing variation in this manner leads to increasedthroughput, reduced cost, higher device performance, etc., all of whichequate to increased profitability.

Processing tools are routinely calibrated to reduce process variations.Test runs, such as qualification runs or experimental runs, are commonlyconducted to calibrate the processing tools or diagnose processingproblems associated with the processing tools. The tests runs aretypically triggered at scheduled intervals, such as every 24 hours, orare initiated by the occurrence of selected events, such as preventativemaintenance events. Unfortunately, these test runs are initiated atscheduled times or events regardless of whether the processing toolsrequire calibration. As such, valuable time and resources, in the formof wafers, for example, may be wasted, in instances where calibration ofthe processing tools may not be necessary.

The present invention is directed to overcoming, or at least reducingthe effects of, one or more of the problems set forth above.

SUMMARY OF THE INVENTION

In one embodiment of the present invention, a method is provided forinitiating test runs based on a fault detection result. The methodcomprises receiving operational data associated with processing of aworkpiece by a processing tool, processing the operational data todetermine fault detection results; and causing a test run to beperformed based on at least a portion of the fault detection results.

In another embodiment of the present invention, an apparatus is providedfor initiating test runs based on a fault detection result. Theapparatus comprises an interface communicatively coupled to a controlunit. The interface is adapted to receive operational data associatedwith processing of a workpiece by a processing tool. The control unit isadapted to determine a health value associated with the processing toolbased at least on the received operational data and cause a test run tobe performed based on the determined health value.

In a further embodiment of the present invention, an article comprisingone or more machine-readable storage media containing instructions isprovided for initiating test runs based on a fault detection result. Theone or more instructions, when executed, enable the processor to receiveoperational data associated with processing of a workpiece by aprocessing tool, determine a health value associated with the processingtool based at least on the received operational data and cause a testrun to be performed based on the determined health value.

In a further embodiment of the present invention, a system is providedfor initiating test runs based on a fault detection result. The systemcomprises a processing tool and a fault detection and classificationunit. The processing tool is adapted to provide operational dataassociated with processing of a workpiece. The fault detection andclassification unit is adapted to receive the operational data,determine a health value associated with the processing tool based atleast on the received operational data and cause a test run to beperformed based on the determined health value.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 illustrates an industrial system, including an APC framework, inaccordance with one embodiment of the present invention;

FIG. 2 illustrates a flow diagram of a method that may be implemented inthe system of FIG. 1, in accordance with one embodiment of the presentinvention; and

FIG. 3 illustrates a flow diagram of an alternative embodiment of amethod that may be implemented in the system of FIG. 1.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Illustrative embodiments of the invention are described below. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. It will of course be appreciated thatin the development of any such actual embodiment, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which will vary from one implementation toanother. Moreover, it will be appreciated that such a development effortmight be complex and time-consuming, but would nevertheless be a routineundertaking for those of ordinary skill in the art having the benefit ofthis disclosure.

Turning now to the drawings, and specifically referring to FIG. 1, ablock diagram of a system 100 is illustrated, in accordance with oneembodiment of the present invention. The system 100, in the illustratedembodiment, includes at least one process operation 102 that may be partof an industrial process, such as a semiconductor fabrication process, aphotographic process, a chemical process, or any other process in whicha plurality of variables, such as temperature, tool parameters, pressurelevel, chemical compositions, and the like may be monitored andanalyzed. The variables may be monitored and analyzed, for example, todetect faults and/or classify the detected faults.

In the system 100, the process operation 102 may be performed using oneor more processing tools 105. Generally, the particular type of processoperation 102 that is performed and the type of processing tool(s) 105employed in that process operation 102 depend on the particularimplementation. For example, in the context of a chemical industrialprocess, the process operation 102 may include processing a polymer. Inthe context of a photographic process, the process operation 102 may,for example, include processing film.

In one embodiment, the process operation 102 depicted in FIG. 1 mayperform a semiconductor fabrication process, which, for example, may bepart of an overall semiconductor process flow. To this extent, theprocessing tool 105 may take the form of any semiconductor fabricationequipment used to produce a processed workpiece, such as a siliconwafer. The semiconductor process may be utilized to produce a variety ofintegrated circuit products including, but not limited to,microprocessors, memory devices, digital signal processors, applicationspecific integrated circuits (ASICs), or other similar devices. Anexemplary processing tool 105 may include an exposure tool, an etchtool, a deposition tool, a polishing tool, a rapid thermal annealprocessing tool, a test-equipment tool, an ion implant tool, a packagingtool and the like. It should be appreciated that the processing tool 105need not necessarily be limited to the processing of silicon wafers, butmay produce a variety of different types of commercial products withoutdeparting from the spirit and scope of the present invention.

In the system 100 of FIG. 1, the processing tool 105 has an associatedequipment interface 110, and a metrology tool 112 has an associatedequipment interface 113, for interfacing with an Advanced ProcessControl (APC) framework 120. In one embodiment, the metrology tool 112measures aspects of the workpieces that are processed in the processoperation 102. In the context of a semiconductor manufacturing process,the metrology tool 112 may provide wafer-related data that characterizesthe quality of the wafer that is processed by the processing tool 105.The wafer product data can be generated from specific quantitativeand/or qualitative measurements that are taken from the wafer by themetrology tool 112. For example, the wafer product data may include filmthickness measurements, line width measurements, and/or overlay offsetmeasurements of the wafer. It will be appreciated that these specificmeasurements that define the wafer product data are merely exemplary.Accordingly, various other measurements may also be taken to determinewhether the wafers that are being processed by the processing tool 105possess the quantitative or qualitative characteristics desired. Thespecific manner in which the wafer product data is obtained by themetrology tool 112 is well known to those of ordinary skill in the artand the details of such will not be discussed herein to avoidunnecessarily obscuring the present invention.

The equipment interface 113 may receive metrology data from themetrology tool 112 and communicate this data to the APC framework 120,which may include a control unit 155 for managing at least the overalloperations of the APC framework 120. In addition, the processing tool105 or a sensor external (not shown) to the processing tool 105 mayprovide data related to the processing of the workpieces (e.g.,semiconductor wafers) to the APC framework 120. The data provided by theprocessing tool 105 is hereinafter referred to as “trace data,” which,in one embodiment, may be provided in substantially real time. Themetrology data, trace data associated with the operating states of theprocessing tool 105, or any other data related to the processing of theworkpieces, is hereinafter referred to as “operational data.”

The system 100 may include a manufacturing execution system (MES) 115that is coupled to the APC framework 120. The manufacturing executionsystem 115 may, for example, determine the processes that are to beperformed by the processing tool 105, when these processes are to beperformed, how these processes are to be performed, etc. In oneembodiment, the MES 115 communicates directly with the equipmentinterfaces 110, 113. The process control unit 155 of the APC framework120, in one embodiment, aids the processing tool 105, through a feedback(or feedforward) process, towards performing a desired process tothereby achieve a desired result.

An exemplary APC framework 120 that may be suitable for use in themanufacturing system 100 may be implemented using the Catalyst systemoffered by KLA-Tencor, Inc. The Catalyst system uses SemiconductorEquipment and Materials International (SEMI) Computer IntegratedManufacturing (CIM) Framework compliant system technologies and is basedon the Advanced Process Control (APC) Framework. CIM (SEMIE81-0699-Provisional Specification for CIM Framework DomainArchitecture) and APC (SEMI E93-0999-Provisional Specification for CIMFramework Advanced Process Control Component) specifications arepublicly available from SEMI, which is headquartered in Mountain View,Calif.

The system 100, in the illustrated embodiment, includes a faultdetection and classification (FDC) unit 150 that is coupled to the APCframework 120 via an interface 145. The interface 145 may be anyacceptable structure(s) that allow(s) the FDC unit 150 to communicatewith other devices. The FDC unit 150 includes a control unit 172 formanaging the overall operations and executing one or more softwareapplications resident in a storage unit 174.

The FDC unit 150, in one embodiment, detects faults associated with theprocessing tool 105. In another embodiment, the FDC unit 150 mayclassify one or more of the detected faults. Although in the illustratedembodiment, the FDC unit 150 detects faults at the “tool” level, inalternative embodiments, the FDC unit 150 may detect faults at anydesirable level, including at a sensor level, process level, systemlevel, and the like.

Faults may occur in a manufacturing process for various reasons,including occurrence of an unknown disturbance, hardware failure,depletion of resources (e.g., gases, liquids, chemicals), and the like.The faults may be detected in several ways, including detecting a faultbased on analyzing metrology data provided by the metrology tool 112and/or trace data provided by the processing tool 105. The FDC unit 150,for example, may detect a fault associated with the processing tool 105if the received metrology data indicates that values measured from theworkpieces are outside an acceptable range. The FDC unit 150, in anotherembodiment, may also detect a fault based on comparing the receivedtrace data from the processing tool 105 to fault model data. The faultmodel data includes operational data of other similar-type tools, whereit was previously known that such tools had operated within acceptableoperational limits.

The system 100 in the illustrated embodiment includes one or morevirtual sensors 175, which may be implemented in software, hardware or acombination thereof. The virtual sensor(s) 175 may collect dataassociated with a variety of elements or components in the system 100and provide the collected data to the FDC unit 150. For example, in oneembodiment, the virtual sensor(s) 175 may provide information regardingrun-to-run processing, such as disparities between runs. To providecomparison of run-to-run data, the APC framework 120 may store data fromthe past runs for a basis of comparison to other runs. In oneembodiment, the APC framework 120 may receive data associated with theprevious process step (i.e., feed forward data) and provide the data tothe FDC unit 150. In one embodiment, the virtual sensor(s) 175 mayprovide data regarding the performance level (or state) of the controlunit 155. In alternative embodiments, the virtual sensor(s) 175 maygather other types of data, depending on the particular implementation.

As described in more detail below, in accordance with one or moreembodiments of the present invention, the results from the FDC unit 150are provided to a test run (TR) module 180, which determines whether itis desirable to perform one or more test runs, for example, to calibratethe processing tool 105 based at least on the FDC results. A variety oftest runs may be performed, including a qualification run and anexperimental run. Qualification runs may be runs that are regularlyscheduled to initialize the starting state of the processing tool 105 orto calibrate the processing tool 105 to a desired state(s). Experimentalruns may include runs that are performed for a special or specificpurpose, such as to diagnose potential problems with the processing(e.g., to determine if the cause of the problem is the processing tool105 itself or a defect in the incoming workpiece). In an alternativeembodiment, the FDC unit 150 detects and classifies faults, and the TRmodule 180 determines which types of test runs to perform based on theclassification of the faults.

It should be appreciated that the illustrated components shown in theblock diagram of the system 100 in FIG. 1 are illustrative only, andthat, in alternative embodiments, additional or fewer components may beutilized without deviating from the spirit or scope of the invention.For example, in one embodiment, the MES 115 may interface with the APCframework 120 through an associated equipment interface (not shown).Furthermore, in one embodiment, the various components of the system100, such as the tools 105, 112 may interface with the APC framework 120through a common equipment interface. Additionally, it should be notedthat although various components, such as the equipment interfaces 110,113 of the system 100 of FIG. 1 are shown as stand-alone components, inalternative embodiments, such components may be integrated into theprocessing tools 105 or metrology tool 112. Furthermore, selectedelements of the system 100, such as the control unit 155 and the TRmodule 180, may be implemented in a single/common device, or the TRmodule 180 and the FDC unit 150 may be implemented in a common device.Similarly, the virtual sensor(s) 175 may be integrated with the FDC unit150, the control unit 155, or the TR module 180. In one embodiment, thesystem 100 of FIG. 1 may be implemented without an APC framework 120.

Referring now to FIG. 2, a flow diagram of a method that may beimplemented in the system 100 of FIG. 1 is illustrated, in accordancewith one embodiment of the present invention. The processing tool 105processes (at 205) a workpiece (e.g., a wafer in the context of asemiconductor fabrication process). In one embodiment, the processingtool 105 may process a batch of workpieces.

As the workpiece is processed (at 205), the processing tool 105 mayprovide operational data, in the form of metrology and/or trace data,associated with the processing of the wafer to the FDC unit 150. Inanother embodiment, the metrology tool 112 may provide metrology dataassociated with the processing of the workpiece (at 205) to the FDC unit150.

The FDC unit 150 receives (at 210) the operational data associated withthe processing of the workpiece. Receiving the operational data (at210), in one embodiment, may include receiving (at 211) trace data fromthe processing tool 105. In one embodiment, the FDC unit 150 may receive(at 212) metrology data. In another embodiment, the FDC unit 150 mayreceive (at 213) data provided by the virtual sensor(s) 175.

The FDC unit 150 determines (at 215) a value associated with the healthof the processing tool 105 based on the received operational data. Onetechnique for determining the tool health value is to employ amultivariate tool health model (not shown) that is adapted to predictthe expected operating parameters of the processing tool 105 during theprocessing of workpieces. If the actual tool parameters are close to thepredicted tool parameters, the processing tool 105 may have a highhealth rating (i.e., the tool is operating as expected). As the gapbetween the expected tool parameters and the actual tool parameterswidens, the tool health rating decreases.

The FDC unit 150 provides (at 222) the tool health value to the TRmodule 180. The TR module 180 receives (at 225) an indication for a testrun. The indication, for example, may have been scheduled to occur atpreselected time intervals or they may be event driven, such as by aperiodically scheduled preventative maintenance event. The TR module180, in one embodiment, may monitor for requests to perform test runs.

If the TR module 180 receives a test run indication, then the TR module180 determines (at 227) whether the health of the processing tool 105 isat an acceptable level. In one embodiment, the tool health value may becompared to a threshold value to determine if the processing tool 105 isoperating at an acceptable level. In one embodiment, the threshold valuemay be a range of values, where the range of values defines theacceptable operating range of the processing tool 105.

Once the TR module 180 receives the test run indication, and determines(at 227) that the processing tool 105 is not operating at an acceptablelevel, then the TR module 180 performs (at 230) the test run. The TRmodule 180 may perform the test run in this case because the tool healthvalue indicates that the processing tool 105 is not operating as desiredand thus, for example may need to be recalibrated. In one embodiment,test run indications may not be employed, in which case the TR module180 may schedule a test run or runs based on determining that the toolhealth is not at an acceptable level. That is, the TR module 180 mayschedule a test run or runs automatically after determining that thetool health value is below the preselected threshold value. Upon thecompletion of the test run, the process continues (at 232) with normaloperation.

If, after receiving the test run indication, the TR module 180determines (at 227) that the processing tool 105 is operating at anacceptable level, then the TR module 180 disregards (at 235), oralternatively postpones, the test run. The TR module 180 may not performthe test run in this case because the tool health value indicates thatthe processing tool 105 is operating as desired, and that recalibrationor preventative maintenance event may not be needed. As such, the TRmodule 180 saves time and resources by skipping or postponing test runsthat add little value, in view of the fact that the processing tool 105is operating as desired. Postponing the test runs allows the processingtool 105 to be operated on a more flexible schedule (i.e., the test runsmay be deferred for a more convenient time), thereby allowing theprocessing tool 105 to continue operating without any immediateinterruptions.

In one embodiment, one or more of the acts described in the method ofFIG. 2 can be implemented in substantially real time. Additionally, theacts of the method of FIG. 2 may vary depending on the arrangement ofvarious components of the system 100 of FIG. 1. For example, if the TRmodule 180 is implemented within the FDC unit 150, then the act ofproviding the tool health value (at 222) may be omitted. Similarly,depending on the arrangement of the various components of the system 100of FIG. 1, the acts of the method of FIG. 2 may vary from oneimplementation to another.

Referring now to FIG. 3, a flow diagram of an alternative embodiment ofa method is illustrated. The processing tool 105 processes (at 310) aworkpiece. The FDC unit 150 receives (at 315) the operational dataassociated with the processing of the workpiece. The various types ofoperational data are described above, and are not repeated herein.

The FDC unit 150 determines (at 320) if one or more faults associatedwith the processing of the workpiece occurred based on the operationaldata. The FDC unit 150 may detect faults in one of a variety of ways,including compare at least a portion of the operational data to one ormore fault models (not shown). In another embodiment, detecting thefaults may include determining if the operational data (e.g., metrologydata) indicates that the measured characteristics of the processedworkpiece are within an acceptable range. The types of faults that maybe detected by the FDC unit 150 include processing and/or operationalfaults in the industrial process. In the context of a semiconductorfabrication process, examples of processing faults may include, but arenot necessarily limited to, non-optimal preheating of the chamber,catastrophic failure where a broken wafer is detected, abnormal nitrogen(N2) flow rate, temperature overshoots at the top of a ramp, tubetemperature measurement drifts, etc. Examples of operational faultsdetected may include interrupted/resumed processing, no wafer sleuth orimproper wafer sleuth prior to Rapid Thermal Anneal (RTA), etc.

The FDC unit 150 classifies (at 325) the one or more detected faults.Classifying the detected faults(s) (at 325) may include determining atleast one cause of the detected fault(s), a process sometimes alsoreferred to as “classification.” In one embodiment, the FDC unit 150determines one or more possible causes based on a fault distributionchart, which may be generated by the fault detection and classificationunit 150 using a well-known technique of principal component analysis.

The TR module 180 selects (at 330) one or more test runs to performbased the on classification of the fault(s). In one embodiment, the TRmodule 180 may include a table that associates, for example, aparticular type of experimental run with a particular type of fault thatis detected by the FDC unit 150. Thus, once a fault is detected, basedon the table, the TR module 180 may perform the experimental run that isassociated with the detected fault. The experimental run may beperformed, for example, to diagnose a cause of a problem encounteredwith the processing of the workpiece or workpieces. That is, theexperimental runs may be performed to narrow or isolate the potentialcauses of the problem encountered. For example, an experimental run mayrequire running the test workpieces with a different type of film, ascompared to a previously used film, to isolate a cause of a problemdetected. The types of experimental runs performed will depend on theparticular circumstances and the objective sought. In one embodiment, aplurality of experimental runs may be associated with a given fault,where each one of the associated experimental runs is performed inresponse to detecting that fault.

For reasons explained above, in accordance with one or more embodimentsof the present invention, the TR module 180 performs one or more testruns based on the results provided by the FDC unit 150. In this manner,the FDC unit 150 causes the test runs to be performed on an as-neededbasis rather than at every scheduled interval. This can reduce theoverall number of test runs that may need to be performed, therebyresulting in savings of time and resources, such as workpieces.Additionally, in one embodiment, the results from the FDC unit 150 maybe utilized to determine which test runs need to be performed and atwhat times. That is, based on the faults classified by the FDC unit 150,the TR module 180 may identify one or more experimental runs that shouldbe performed.

The various system layers, routines, or modules may be executable by thecontrol units 155, 172 (see FIG. 1). As utilized herein, the term“control unit” may include a microprocessor, a microcontroller, adigital signal processor, a processor card (including one or moremicroprocessors or controllers), or other control or computing devices.The storage unit 174 (see FIG. 1) referred to in this discussion mayinclude one or more machine-readable storage media for storing data andinstructions. The storage media may include different forms of memoryincluding semiconductor memory devices such as dynamic or static randomaccess memories (DRAMs or SRAMs), erasable and programmable read-onlymemories (EPROMs), electrically erasable and programmable read-onlymemories (EEPROMs) and flash memories; magnetic disks such as fixed,floppy, removable disks; other magnetic media including tape; andoptical media such as compact disks (CDs) or digital video disks (DVDs).Instructions that make up the various software layers, routines, ormodules in the various systems may be stored in respective storagedevices. The instructions when executed by a respective control unitcause the corresponding system to perform programmed acts.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown, other than as describedin the claims below. It is therefore evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

1. A method, comprising: receiving operational data associated with processing a workpiece by a processing tool; processing the operational data to determine fault detection results; causing a test run to be performed based on at least a portion of the fault detection results; wherein processing comprises detecting a fault associated with the processing of the workpiece and classifying the fault and determining a particular type of test run to be performed based on the classification of the fault; and further comprising receiving operational data associated with a processing of the workpiece from an upstream processing tool, and determining a type of test run to be performed based on the operational data received from the processing of the workpiece from the upstream processing tool.
 2. The method of claim 1, wherein processing the operational data further comprises processing the operational data to determine a health value associated with the processing tool and causing the test run to be performed further comprises causing the test run to be performed based on the determined health value.
 3. The method of claim 2, wherein the test run is caused to be performed based on determining if the health value is greater than a pre-selected threshold.
 4. The method of claim 1, wherein causing the test run to be performed comprises causing at least one of a qualification run and experimental run to be performed.
 5. The method of claim 1, wherein receiving the operational data comprises receiving at least one of metrology data associated with the processed workpiece and trace data associated with the processing tool.
 6. The method of claim 1, wherein receiving the operational data comprises receiving the operational data associated with processing of a semiconductor wafer.
 7. A method, comprising: receiving operational data associated with processing a workpiece by a processing tool; processing the operational data to determine fault detection results; causing a test run to be performed based on at least a portion of the fault detection results; and calibrating the processing tool based on causing the test run to be performed.
 8. An apparatus, comprising: an interface adapted to receive operational data associated with processing of a workpiece by a processing tool; and a control unit communicatively coupled to the interface, the control unit adapted to: determine a health value associated with the processing tool based at least on the received operational data; cause a test run to be performed based on the determined health value; and calibrate the processing tool based on causing the test run to be performed.
 9. The apparatus of claim 8, wherein the control unit is adapted to cause the test run to be performed based on determining if the health value is greater than a pre-selected threshold.
 10. The apparatus of claim 8, wherein the control unit is adapted to further detect a fault associated with the processing of the workpiece and classifying the fault.
 11. The apparatus of claim 10, wherein the control unit is adapted to determine a particular type of test run to be performed based on the classification of the fault.
 12. The apparatus of claim 11, wherein the control unit is adapted to receive operational data associated with a processing of the workpiece from an upstream processing tool, and determine a particular type of test run to be performed based on the operational data received from the processing of the workpiece from the upstream processing tool.
 13. The apparatus of claim 8, wherein the processing tool further comprises an associated process controller, wherein the control unit is adapted to receive data from a virtual sensor regarding the operational state of the process controller and to cause at least one of a qualification run and experimental run to be performed based on the data received from the virtual sensor.
 14. An article comprising one or more machine-readable storage media containing instructions that when executed enable a processor to: receive operational data associated with processing of a workpiece by a processing tool; determine a health value associated with the processing tool based at least on the received operational data; and cause a test run to be performed based on the determined health value; and calibrate the processing tool based on causing the test run to be performed.
 15. The article of claim 14, wherein the instructions when executed enable the processor to cause the test run to be performed based on determining if the health value is greater than a pre-selected threshold.
 16. The article of claim 14, wherein the instructions when executed enable the processor to detect a fault associated with the processing of the workpiece and classifying the fault.
 17. The article of claim 16, wherein the instructions when executed enable the processor to receive operational data associated with processing of the workpiece from an upstream processing tool, and determine a particular type of test run to be performed based on the operational data received from the processing of the workpiece from the upstream processing tool.
 18. The article of claim 14, wherein the instructions when executed enable the processor to cause at least one of a qualification run and experimental run to be performed.
 19. The article of claim 14, wherein the instructions when executed enable the processor to receive the operational data associated with processing of a semiconductor wafer. 